Confronting the employment standards enforcement gap: Exploring the potential for union engagement with employment law in Ontario, Canada
Bibliographic record
Abstract
Employment standards (ES) are legislated standards that set minimum terms and conditions of employment in areas such as wages, working time, vacations and leaves, and termination and severance. In Canada, the majority of workers rely on ES for basic regulatory protection; however, a significant ‘enforcement gap’ exists. In the province of Ontario, this enforcement gap has been exacerbated in recent years due to the deregulation of ES through inadequate funding, workplace restructuring, legislative reforms that place greater emphasis on individualized complaints processes and voluntary compliance, and a formal separation of unions from ES enforcement. The implications of these developments are that, increasingly, those in precarious jobs, many of whom lack union representation, are left with insufficient regulatory protection from employer non-compliance, further heightening their insecurity. Taking the province of Ontario as our focus, in this article we critically examine alternative proposals for ES enforcement, placing our attention on those that enhance the involvement of unions in addressing ES violations. Through this analysis, we suggest that augmenting unions’ supportive roles in ES enforcement holds the potential to enhance unions’ regulatory function and offers a possible means to support the ongoing efforts of other workers’ organizations to improve employer compliance with ES.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".